Relationship Between Glycemic Control and Diabetes-Related Hospital Costs in Patients with Type 1 or Type 2 Diabetes Mellitus

BACKGROUND: Diabetes mellitus requires continuous medical care and patient self-management in order to prevent short-term complications and decrease the risk of long-term complications, which can result in substantial increases in the total economic burden of the disease. Findings from randomized clinical trials have shown that improved glycemic control may reduce the risk of long-term complications as long as a target for hemoglobin A1c is not set below 7% for intensive glycemic control. However, limited data from clinical practice are available regarding the relationship between glycemic control and medical costs associated with diabetes care. OBJECTIVES: To assess the potential relationships between glycemic levels, diabetes-related hospitalizations, and hospital costs among adult patients with either type 1 or type 2 diabetes mellitus who were assigned to a primary care provider (PCP) in a clinic that was affiliated with a managed care organization (MCO). METHODS: A retrospective cohort analysis was conducted using data from approximately 200,000 members of the Fallon Clinic Health Plan who were assigned to a clinic PCP at any time during a 5-year study period beginning January 1, 2002, and ending December 31, 2006. Patients aged 30 years or older with at least 2 medical claims with any listed diagnosis of diabetes mellitus (ICD-9-CM code 250.xx) during the study period and 2 or more A1c values within 1 year of each other during the study period (mean 7.6 tests over 39 months; median=6.8), were identified and stratified into 1 of 5 groups defined by 1% increments of A1c, based on their mean A1c values during the entire study period. A1c data were available only for tests ordered by a clinic provider; tests ordered by other specialists in the MCO's network were absent from the database. The study follow-up period started with each patient's first A1c test (index date) and continued until plan disenrollment, death, or December 31, 2006, whichever was earlier (end date), regardless of when the diagnosis of diabetes mellitus was made. Study measures included the proportion of patients with 1 or more diabetes-related hospitalizations, number of diabetes-related inpatient stays, and the associated estimated hospitalization costs over the follow-up period. Diabetes-related hospitalizations were identified based on a diagnosis, in any of 10 diagnosis fields, for 1 of 16 selected complications of diabetes identified by the authors. Hospital costs were estimated using discharge data (diagnoses and costs calculated from cost-to-charge ratios) contained in the 2004 Healthcare Cost and Utilization Project (HCUP) database and inflated to 2007 dollars using the medical care component of the Consumer Price Index. Multivariate models controlled for age, sex, number of A1c tests, diagnosis of cancer, and follow-up time. A multivariate logistic regression analysis was conducted with the occurrence of at least 1 diabetes-related hospital admission as the dependent variable. In the logistic regression analysis, follow-up time was defined as time from the index date to the date of the first diabetes-related hospitalization, plan disenrollment, death, or the study end date, whichever occurred first. A generalized linear model with a Poisson distribution and a log link was employed to estimate the rate of hospital admissions. In the Poisson regression analysis, follow-up time was defined as duration of the entire study follow-up period and was an offset variable. Costs were estimated using a 2-part model: first, we calculated the probability of having a hospitalization, as determined by the logistic regression above; second, a generalized linear model with a negative binomial distribution and a log link was used to predict the mean cost of diabetes-related hospitalizations only for patients with an inpatient stay, with the duration of the entire study follow- up period as an offset variable. We calculated the mean per patient cost of diabetes-related hospitalizations by multiplying the probability of having a hospitalization (as determined by the first part of the model) by the mean costs for patients who had such admissions (as determined by the second part of the model). RESULTS: 9,887 patients met study selection criteria. Mean A1c level was less than 7% for 5,649 (57.1%) patients, 7% to less than 8% for 2,747 (27.8%), 8% to less than 9% for 1,002 (10.1%), 9% to less than 10% for 312 (3.2%), and 10% or more for 177 (1.8%). Over a mean (median) 40 (40) months of follow-up (interquartile range = 30-50 months), 28.7% (n = 2,838) of patients had 1 or more diabetes-related hospital admissions. In the logistic regression analysis, odds of having at least 1 diabetes-related hospital stay did not significantly differ for patients with mean A1c of less than 7% compared with patients in most higher mean A1c categories (7% to less than 8%, 8% to less than 9%, or 9% to less than 10%); however, odds of having a diabetes-related hospitalization were significantly higher for patients with mean A1c of 10% or more compared with patients with mean A1c of less than 7% (odds ratio = 2.13, 95% confidence interval = 1.36-3.33). In the negative binomial regression analysis of those with at least 1 hospital admission, estimated costs per hospitalized patient increased by mean A1c level. In the Poisson regression analysis, the rate of diabetes-related hospitalizations significantly increased by A1c level (13 per 100 patient-years for patients with mean A1c of less than 7% vs. 30 per 100 patient-years for mean A1c of 10% or more when covariates were held at mean levels, Pless than0.001). In the 2-part model results, adjusted mean estimated costs of diabetes-related hospitalizations per study patient were $2,792 among those with mean A1c of less than 7% and $6,759 among those with mean A1c of 10% or more. CONCLUSIONS: In this managed-care plan, the odds of having at least 1 diabetes-related hospitalization were not significantly associated with higher mean A1c except for patients with mean A1c of at least 10%. However, higher mean A1c levels were associated with significantly higher estimated hospitalization costs among those with at least 1 hospitalization and with higher rates of diabetes-related hospital utilization per 100 patient-years.

• Diabetes mellitus imposes a substantial economic burden on society.
The American Diabetes Association has estimated that the cost of diabetes in the United States was approximately $174 billion in 2007. • Previous studies using claims data showed that improved glycemic control was associated with fewer primary care visits and inpatient admissions. Wagner et al. (2001), using claims data from a large Washington HMO from 1992-1996, found that patients with predominantly type 2 diabetes whose hemoglobin A1c improved had lower total health care costs ($685 to $950 less per year) than those without A1c improvement. Shetty et al. (2005) found that in a large MCO, patients with type 2 diabetes whose A1c exceeded a target level (7%) had 32% higher diabetes-related costs than patients at or below the target level (P < 0.001). ABSTRACT BACKGROUND: Diabetes mellitus requires continuous medical care and patient self-management in order to prevent short-term complications and decrease the risk of long-term complications, which can result in substantial increases in the total economic burden of the disease. Findings from randomized clinical trials have shown that improved glycemic control may reduce the risk of long-term complications as long as a target for hemoglobin A1c is not set below 7% for intensive glycemic control. However, limited data from clinical practice are available regarding the relationship between glycemic control and medical costs associated with diabetes care.

Relationship Between Glycemic Control and Diabetes-Related Hospital Costs in Patients with Type 1 or Type 2 Diabetes Mellitus
OBJECTIVE: To assess the potential relationships between glycemic levels, diabetes-related hospitalizations, and hospital costs among adult patients with either type 1 or type 2 diabetes mellitus who were assigned to a primary care provider (PCP) in a clinic that was affiliated with a managed care organization (MCO).
METHODS: A retrospective cohort analysis was conducted using data from approximately 200,000 members of the Fallon Clinic Health Plan who were assigned to a clinic PCP at any time during a 5-year study period beginning January 1, 2002, and ending December 31, 2006. Patients aged 30 years or older with at least 2 medical claims with any listed diagnosis of diabetes mellitus (ICD-9-CM code 250.xx) during the study period and 2 or more A1c values within 1 year of each other during the study period (mean 7.6 tests over 39 months; median=6.8), were identified and stratified into 1 of 5 groups defined by 1% increments of A1c, based on their mean A1c values during the entire study period. A1c data were available only for tests ordered by a clinic provider; tests ordered by other specialists in the MCO's network were absent from the database. The study follow-up period started with each patient's first A1c test (index date) and continued until plan disenrollment, death, or December 31, 2006, whichever was earlier (end date), regardless of when the diagnosis of diabetes mellitus was made. Study measures included the proportion of patients with 1 or more diabetes-related hospitalizations, number of diabetes-related inpatient stays, and the associated estimated hospitalization costs over the follow-up period. Diabetes-related hospitalizations were identified based on a diagnosis, in any of 10 diagnosis fields, for 1 of 16 selected complications of diabetes identified by the authors. Hospital costs were estimated using discharge data (diagnoses and costs calculated from cost-to-charge ratios) contained  In the logistic regression analysis, odds of having at least 1 diabetes-related hospital stay did not significantly differ for patients with mean A1c of < 7% compared with patients in most higher mean A1c categories (7% to < 8%, 8% to < 9%, or 9% to < 10%); however, odds of having a diabetes-related hospitalization were significantly higher for patients with mean A1c of 10% or more compared with patients with mean A1c of < 7% (odds ratio = 2.13, 95% confidence interval = 1.36-3.33).
In the negative binomial regression analysis of those with at least 1 hospital admission, estimated costs per hospitalized patient increased by mean A1c level. In the Poisson regression analysis, the rate of diabetes-related hospitalizations significantly increased by A1c level (13 per 100 patient-years for patients with mean A1c of < 7% vs. 30 per 100 patient-years for mean A1c of 10% or more when covariates were held at mean levels, P<0.001). In the 2-part model results, adjusted mean estimated costs of diabetes-related hospitalizations per study patient were $2,792 among those with mean A1c of < 7% and $6,759 among those with mean A1c of 10% or more. CONCLUSIONS: In this managed-care plan, the odds of having at least 1 diabetes-related hospitalization were not significantly associated with higher mean A1c except for patients with mean A1c of at least 10%. However, higher mean A1c levels were associated with significantly higher estimated hospitalization costs among those with at least 1 hospitalization and with higher rates of diabetes-related hospital utilization per 100 patient-years. D iabetes mellitus is a growing public health problem that adversely affects the lives of millions of individuals around the world. 1,2 This disease requires continuous medical care and patient self-management in order to prevent short-term complications and decrease the risk of long-term complications. 3 These complications can result in substantial increases in the total economic burden of the disease. The American Diabetes Association (ADA) has estimated that the annual cost of diabetes in the United States was approximately $174 billion in 2007. 4 Previous studies have found some evidence that better glycemic control among patients with type 2 diabetes may be associated with lower health care resource use and costs. 5-9 Oglesby et al. (2006), using data from October 1, 1998, through April 30, 2003, found that diabetes-related costs were 16% and 20% lower for patients with good control (hemoglobin A1c 7% or less) compared with fair (A1c more than 7% to 9% or less) and poor control (A1c more than 9%), respectively. 9 Aside from that study, previous research examined the association between glycemic control and health care resource use and costs using older data (1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) and variable definitions of glycemic control. For example, Menzin et al. (2001) used data from 1994 through mid-1998 to assess short-term inpatient (hospital or skilled nursing facility) admissions and costs associated with A1c < 8% ("good control"), 8%-10% ("fair control"), and > 10% ("poor control"). 6 The purpose of this study was to examine the relationship between glycemic control and diabetes-related hospitalizations and associated costs among patients with diabetes, using more recent data. Specifically, this study sought to test the Relationship Between Glycemic Control and Diabetes-Related Hospital Costs in Patients with Type 1 or Type 2 Diabetes Mellitus hypothesis that poorer glycemic control is associated with higher rates of diabetes-related hospitalizations and costs among patients with diabetes mellitus treated in clinical practice.

■■ Methods Data Sources
Data from the Fallon Clinic (Worcester, Massachusetts) covering the 5-year period from January 1, 2002, through December 31, 2006 (study period), were used to explore the relationship between glycemic control and diabetes-related hospitalizations. The Fallon Clinic is a multispecialty group clinic with a predominantly managed care population of approximately 200,000 patients at the time of study initiation. The study data set consisted of 4 files: an enrollment file, an inpatient hospital claims file that included claims with a place of service indicating an inpatient setting and date of service span exceeding 1 day, a pharmacy file, and a clinical laboratory file. The clinical laboratory file included all tests that were ordered by a Fallon Clinic provider. Patients' data were de-identified, with a unique, encrypted identifier available to link the 4 files. The study was approved by the Fallon Clinic Institutional Review Board.
Because only billed charge data, not cost data, were available in the study database, we used 2004 data from the Healthcare Cost and Utilization Project (HCUP) 10 to estimate costs for hospitalizations related to diabetes, which were assigned to the hospitalizations observed in the claims database. HCUP is a nationally representative, multihospital database that contains International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes and charges, as well as a separate file to convert charges to costs using institutionspecific cost-to-charge ratios (i.e., a single cost-to-charge ratio for each hospital, applied to all admissions for that hospital).

Patients and Follow-Up
Because laboratory data were available only for tests ordered by a Fallon Clinic physician, the initial sample selection was limited to patients with Fallon Clinic Health Plan membership assigned to a Fallon Clinic primary care provider (PCP) for at least 1 month during the 5-year study period. From that group, patients were selected for study if they were aged 30 years or older as of January 1, 2002, had at least 2 medical claims with a diagnosis of type 1 or type 2 diabetes (ICD-9-CM codes 250.xx) during the 5-year study period, had at least 2 A1c values measured within any 1-year period during the study period (in order to exclude patients who received an A1c test for screening only), had at least 12 months of follow-up after their first A1c test, and had no hospitalizations with overlapping dates of service. Specifically, for patients with more than 1 hospitalization record, each record could not have an admission date falling in between the admission and discharge date of another record.
Each eligible study patient's A1c level was defined as the • This study was conducted using data for 5 years from 2002 through 2006, a sample of 9,887 patients with either type 1 or type 2 diabetes, and an extended follow-up period (over 3 years, on average). • The proportion of patients with at least 1 broadly defined diabetesrelated hospitalization was not significantly different across mean A1c levels measured over a time period of up to 5 years, after controlling for differences among study cohorts in age, sex, number of A1c tests, cancer diagnosis, and follow-up time, except that patients with mean A1c levels of 10% or more were more likely to be hospitalized for a diabetes-related diagnosis than patients with mean A1c of < 7%. • The adjusted hospitalization rate was significantly higher for patients with worse glycemic control (13 per 100 patient-years for mean A1c < 7% vs. 30 per 100 patient-years for mean A1c ≥ 10%, P < 0.001). • Among those with at least 1 hospitalization, estimated cost per hospitalized patient increased with higher mean A1c levels. Average estimated costs of diabetes-related hospitalizations per study patient, adjusted for differences across study cohorts in potential confounding factors, were $2,792 for patients with mean A1c < 7% and $6,759 for patients with mean A1c of 10% or more, a mean difference of $3,967.

What this study adds
calculated the difference between these 2 mean costs as the estimated attributable cost. Lastly, we assigned the attributable cost found in HCUP to each diabetes-related hospitalization in the claims database. For patients with more than 1 nonprimary diabetes-related diagnosis, the first-named diagnosis was used. For example, if both the second and third diagnoses were diabetes-related, the second diagnosis was used in the HCUP estimating procedure. Costs from HCUP were adjusted to 2007 dollars using the medical care component of the Consumer Price Index. Cost estimates for diabetes-related hospitalizations were used to calculate the per patient hospitalization costs.

Statistical Analyses
Descriptive statistics for age, sex, and duration of follow-up were calculated for each mean A1c level. The proportion of patients with 1 or more diabetes-related hospitalizations and the number of such admissions per 100 patient-years (admission rate) were assessed on both an unadjusted and adjusted basis. Adjusted proportions were estimated using a multivariate logistic regression model controlling for age, sex, number of A1c tests, diagnosis of cancer (based on ICD-9-CM codes 140.xx-208.xx), and time from the index date (i.e., date of the first A1c test) to the date of the first diabetes-related hospitalization, disenrollment, death, or the study end date, whichever occurred first. Adjusted average rates of admission were estimated using a generalized linear model with a Poisson distribution and a log link with the duration of the entire study follow-up period (i.e., time from index date until disenrollment, death, or the study end date, whichever occurred first) as an offset variable. We also controlled for age, sex, number of A1c tests, and diagnosis of cancer. The adjusted average hospitalization cost was estimated using a 2-part model. 11 First, we calculated the probability of having a hospitalization, as determined by the logistic regression above; second, a generalized linear model with a negative binomial distribution and a log link was used to predict the mean cost of diabetes-related hospitalizations only for patients with an inpatient stay, with the duration of the entire study follow-up period as an offset variable, controlling for the same variables listed above. We calculated the mean cost of diabetesrelated hospitalizations per study patient by multiplying the probability of having a hospitalization (as determined by the first part of the model) by the estimated mean cost for patients who had a hospitalization (as determined by the second part of the model). For each analysis, after estimating the model, all covariates except for A1c level were held at their mean values and the expected odds, rates, or costs were calculated for each of the 5 mean A1c levels. All data analyses were conducted using the Statistical Analysis System (SAS) software package, version 9.1 (SAS, Inc., Cary, NC), and a 2-sided P < 0.05 was considered significant. mean of all of his or her A1c values available during the followup period. The study follow-up period began with the date of a patient's first A1c test during his or her enrollment (index date), regardless of the date on which the patient was diagnosed with diabetes, and continued until the disenrollment date from the health plan, death, or the study end date (December 31, 2006), whichever occurred first. Thus, patients had variable lengths of follow-up. Patients were stratified into 5 study groups based on their mean A1c levels: < 7%, 7% to < 8%, 8% to < 9%, 9% to < 10%, and 10% or more, to assess the association of 1% differences in mean A1c with diabetes-related hospitalizations and associated costs.

Study Measures
We evaluated diabetes-related hospitalizations based on any diagnosis listed on inpatient claims, on which a total of 10 diagnosis fields were available. Hospitalizations could occur at any point after the first A1c test and, if the first A1c test preceded the diabetes diagnosis, could have occurred prior to the first diabetes diagnosis date. Using ICD-9-CM codes, we identified 16 diabetes-related complications, both short and long term (Appendix A). The short-term complications were used in a previous study, conducted by the present study's authors, of the association between costs and glycemic control. 6 The longterm complications are common conditions related to diabetes, such as ischemic heart disease, nephropathy, neuropathy, and retinopathy. If one of these conditions was listed as the primary diagnosis or one of the 9 other listed diagnoses, the hospitalization was considered to be diabetes-related.
The study measures for hospitalization included the proportion of patients with 1 or more stays and the number of such hospitalizations per 100 patient-years. Additionally, we assessed the average cost per patient for these hospitalizations. Because cost data were not available in the study database, we applied nationally weighted mean cost data from the 2004 HCUP data based on an assigned reason for hospitalization (1 of 16 diabetes-related complications, Appendix B). We used mean HCUP costs instead of the median value, since the mean better reflects financial impact to the hospital for the average discharge. These data are weighted to represent all national inpatient admissions. If a primary diagnosis was used to identify the hospitalization as diabetes-related, we calculated a mean cost for that admission using all HCUP hospitalizations with the same primary diagnosis. If a nonprimary diagnosis was used to identify the hospitalization as diabetes-related, we included only the estimated attributable cost associated with that admission, as follows. First, we identified all of the diagnosis-related group (DRG) codes for each type of diabetesrelated hospitalization in HCUP separately and combined all diabetes-related hospitalizations with the same DRG code into 1 group. Then, within each DRG, we estimated the average cost for patients with and without the specific diagnosis and

Patient Characteristics
Initial selection of all health plan members aged 18 years or older with at least 1 medical claim for type 1 or type 2 diabetes (ICD-9-CM of 250.xx) and assignment to a Fallon clinic PCP at any time during the study period yielded 16,184 patients. Of these, 9,887 met all selection criteria (Figure 1). The mean (SD) age was 66.6 (12.4) years, and approximately 52% of patients were male (Table 1). The mean (SD) number of A1c tests per patient during the follow-up period was 7.6 (4.3), with an interquartile range (IQR) of 4 to 10 tests and a median of 7 tests. The mean (SD) A1c was 7.0% (1.1%) with IQR of 6.2%-7.5% and median of 6.8%; 4,238 patients (42.9%) had a mean A1c of 7% or more, and 177 patients (1.8%) had a mean A1c of 10% or more. The mean (SD) follow-up duration was approximately 40 (14) months (IQR = 30-50 months).
There were important differences in terms of age (P < 0.001), sex (P = 0.001), and length of the follow-up period (P = 0.002) across study groups defined by mean A1c levels (Table 1). Mean age decreased as mean A1c values increased, from 68.1 years for patients with mean A1c < 7% to 54.0 years for mean A1c of 10% or more. The proportion of patients who were younger than 50 years of age was 8.3% for patients with mean A1c < 7% compared with 42.4% for patients with mean A1c of 10% or more, perhaps reflecting a greater preponderance of patients with type 1 diabetes in the higher A1c group. In addition, the percentage of male patients generally increased with A1c levels (50.2% among patients with mean A1c < 7%, compared with 58.8% among those with mean A1c of 10% or more). Length of follow-up was shorter for patients at higher mean A1c levels; the mean length of the follow-up period ranged from 33.9 months (mean A1c of 10% or more) to 41.7 months (mean A1c of 7% to < 8%). Table 2 shows the most common diagnoses associated with hospitalizations defined as diabetes-related. For patients with mean A1c of < 7% (n = 3,046 hospitalizations), the top 5 diagnoses included ischemic heart disease (44.0%), electrolyte imbalance (13.5%), pneumonia (10.8%), urinary tract infection (9.4%), and stroke (5.8%). For patients with mean A1c of 10% or more (n = 121 hospitalizations), the top 5 diagnoses included ischemic heart disease (28.1%), hyperglycemia (15.7%), electrolyte imbalance (10.7%), urinary tract infection (9.1%), and hypoglycemia (8.3%). Table 3 presents the results of the logistic regression for the proportion of patients with 1 or more hospitalizations and the Poisson regression for the hospitalization rate. A total of 2,841 (28.7%) patients were hospitalized with a diabetes-related diagnosis during follow-up, accounting for 5,874 total hospital stays and 18.0 admissions per 100 patient-years for the sample overall. After controlling for differences among study cohorts in age, sex, number of A1c tests, diagnosis of cancer, and follow-up time, the adjusted proportion of patients with 1 or more diabetes-related hospitalizations was significantly lower for those with a mean A1c < 7% versus those with a mean A1c of 10% or more. When covariates were held at their mean values in the logistic regression equation, the adjusted proportions of patients hospitalized were 19.5% for patients with mean A1c < 7% and 33.9% for patients with mean A1c of 10% or more. Compared with the reference group of patients with mean A1c of < 7%, the adjusted odds ratio for being hospitalized for diabetes-related complications for patients with mean A1c of 10% or more was 2.13 (95% confidence interval [CI] = 1.36-3.33). However, relative to the < 7% group, logistic regression

Relationship Between Glycemic Control and Diabetes-Related Hospital Costs in Patients with Type 1 or Type 2 Diabetes Mellitus
Fallon Clinic patients with a PCP a N = 196,868   revealed no significant difference in the proportions of patients hospitalized in the groups with mean A1c 7% to < 8%, 8% to < 9%, or 9% to < 10%.

Patient Selection Flowchart
In the Poisson regression analysis, there was a significant positive association between hospitalization rate per 100 patient-years and mean A1c level (P < 0.001). On an adjusted basis, patients with mean A1c of 10% or more had 2.25 times (95% CI = 1.87-2.70) as many hospitalizations for diabetesrelated complications as patients with mean A1c of < 7% (29.7 hospitalizations per 100 patient-years vs. 13.2 per 100 patientyears, respectively, when covariates were held at mean values). Patients with mean A1c of 7%-< 8%, 8%-< 9%, and 9%-< 10% had 1.18, 1.64, and 1.72 times as many hospitalizations for diabetes-related complications, respectively, as patients with mean A1c of < 7% (all P < 0.001). Table 4 displays the results of the second part of the 2-part model, a negative binomial regression predicting costs for all patients who had a hospitalization, and shows that among those patients with at least 1 hospital stay, increasing mean A1c level was associated with higher costs per hospitalized patient. In the results derived from multiplying the 2 parts of the model, the adjusted average cost of hospitalization per study   (2002)(2003)(2004)(2005)(2006). f Each patient's A1c level was defined as the mean of all A1c values obtained for that patient over a follow-up period that ranged from a minimum of 12 months to a maximum of 5 years (2002-2006). The means shown in the table rows represent categorizations of each patient based on the patient's mean per test A1c. g Predicted outcomes in equations in which covariates were held at their mean values. A1c = hemoglobin A1c; CI = confidence interval; NA = not applicable.

Logistic Regression Predicting the Probability of Having a Diabetes-Related Inpatient Stay b Poisson Regression Predicting the Number of Diabetes-Related Admissions per 100 Person-Years
can lead to adverse cardiovascular outcomes. [12][13][14] Alternatively, a recent large meta-analysis suggests that such intensive therapy may lower the risk of coronary heart disease but not stroke or overall mortality. 15 This controversy suggests that more research is need to further clarify the risk-benefit ratio for use of intensive treatment to bring A1c levels below 7% in type 2 diabetes. In contrast, for patients with type 1 diabetes, results of the Epidemiology of Diabetes Interventions and Complications study (EDIC), a 9-year follow-up of patients previously enrolled in the Diabetes Control and Complications Trial (DCCT), suggested that patients assigned to intensive glycemic control in the DCCT (mean A1c 7.4% at the end of DCCT observation) had lower rates of cardiovascular disease events after 9 years of post-DCCT follow-up compared with patients assigned to conventional glycemic control in DCCT (mean A1c 9.1% at end of DCCT observation). 16 The findings in this study are consistent with those of other published studies. Wagner et al. (2001) studied patients from a large Washington health maintenance organization from 1992-1997 and found that patients whose A1c improved had lower costs and fewer primary care visits but no significant difference in inpatient admissions. 5 Gilmer et al. (2005) reported that in a large Minnesota health plan, higher A1c in patients with either type 1 or type 2 diabetes predicted higher total health care costs for patients with A1c > 7.5%. 7 However, Gilmer et al. also noted that while A1c is an important clinical predictor of costs, other clinical predictors, such as coronary heart disease, hypertension, and depressive symptoms, are also highly predictive. Shetty et al. (2005) studied patients with type 2 diabetes in a large managed care organization (MCO) during a 1-year patient with A1c < 7% was $2,792, compared with $6,759 for patients with A1c of 10% or more ( Figure 2).

■■ Discussion
Our study demonstrated that in patients with type 1 or type 2 diabetes, the adjusted rates of diabetes-related hospital admissions per 100 patient-years increased significantly with higher levels of A1c. The adjusted rate of diabetes-related hospitalizations for patients with a mean A1c of 10% or more, who represented 1.8% of our sample, was more than twice that of patients with a mean A1c of < 7%. Among patients with at least 1 hospitalization, corresponding average adjusted costs increased with A1c levels. Study patients with mean A1c of 10% or more had higher diabetes-related hospital costs than study patients with mean A1c of < 7%.
In a previous study, we analyzed Fallon Clinic data from 1994 to 1998 and found that diabetes patients with poor glycemic control, defined as mean A1c of more than 10%, had substantially higher hospitalization rates for selected short-term complications compared with those with fair control (mean A1c 8%-10%) or good control (mean A1c < 8%). 6 In the current study, we extended the analysis to a broad list of 16 diabetesrelated diagnoses and found a similar trend: hospitalization rates increase with higher A1c levels. This new analysis evaluated individual point levels for A1c and used the current ADA recommendation of less than 7% as well controlled. 3 However, more recent data suggest that diabetes-related complications may be increased in patients with therapy targeted to very tight glycemic control. Intensive treatment of patients with type 2 diabetes to a target A1c of less than 7% follow-up period and found that diabetes-related costs were 32% higher for patients above the target A1c level (7%) than for patients at or below the target level. 8 Oglesby et al. (2006) found diabetesrelated costs to be 16% and 20% lower for patients with good control compared with fair and poor control, respectively. 9 Our study provided new information that has not been reported in previous medical literature. To our knowledge, this is the first study to show a significant, positive, and graded relationship between 1-point A1c intervals and rates of diabetes-related hospitalizations. Additionally, it was conducted using an extended follow-up period (over 3 years, on average).

Limitations
This study had several limitations. First, the definition of "diabetes-related" was based on up to 10 different diagnoses on inpatient claims and a broad list of diagnoses developed by the authors. Although this method has not been validated, the authors have previously published work using this method. The authors thank Devi Sundaresan for assistance with data collection and Mika K. Green for assistance with the manuscript. An abstract and poster based on this work were presented at the Academy of Managed Care Pharmacy's 2008 Educational Conference, October 15-18, 2008, in Kansas City, Missouri.

DISCLOSURES
Concept and design were performed primarily by Menzin, Friedman, and Zhang. Data collection and writing of the manuscript were performed primarily by Korn, Menzin, and Zhang. Data interpretation was performed primarily by Cohen, Lobo, and Neumann. Menzin, Korn, and Zhang wrote the manuscript, and revisions were made primarily by Menzin and Korn. Second, because the laboratory data file contains only tests ordered by Fallon Clinic providers, tests ordered by nonclinic specialists in the MCO's provider network were missing from the database. The effect of the omission of these tests on study results is unknown. Third, the Fallon Clinic database does not contain information on the duration of patients' disease or the severity of comorbidities that might be potential confounders of study measures used in this analysis. In addition, other factors that may be viewed as confounders, such as type and number of antidiabetic medications, were not controlled for in the analysis because these variables would be expected to be highly correlated with A1c levels and not truly independent.
Fourth, we included both type 1 and type 2 diabetes patients in the study sample because we do not have confidence in distinguishing between types using claims data. A fifth digit ending in zero or 2 includes "Type 2 or unspecified," and if the type is unknown then type 2 is to be coded. 17 The younger age of patients with higher mean A1c levels suggests the possibility of a higher prevalence of patients with type 1 diabetes in that group. The effect of this pattern on study results is unknown. Fifth, calculating a mean of all observed A1c values to classify patients is not sensitive to changes over time in A1c levels, suggesting that future work using alternative approaches (e.g., based on time spent at various levels of glycemic control) may be of interest. Sixth, the study's reliance on claims data rather than medical records data may raise concerns about clinical accuracy. [18][19] However, our previous study, which used 1994-1998 data from the same database, found that roughly 75% of the ICD-9-CM diagnosis codes for hospitalizations matched the actual diagnoses in the medical record. 6 Seventh, we applied standard cost data derived from the national HCUP database to patients from the Fallon Clinic because only billed charge data were available in the database, which may introduce potential bias to the cost analysis. Eighth, this study involved a relatively small sample size for an observational study (e.g., there were only 177 patients in the group with mean A1c of 10% or more). Ninth, this study evaluated the relationships between hospital costs for diabetes-related complications and glycemic control. To the extent that better control may lead to savings in other types of costs (e.g., direct medical costs associated with outpatient services or indirect costs associated with productivity), our analysis may be viewed as conservative.

■■ Conclusions
In a sample of 9,887 managed care patients with either type 1 or type 2 diabetes, we showed a significant, positive, and graded relationship between 1-point A1c intervals and rates of diabetes-related hospitalizations per 100 patient-years. Future research to examine the effects of the severity of diabetes and comorbidities may be important in targeting interventions to improve outcomes and reduce costs.